Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field And CNNs for Stock Return Predictions

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dc.contributor.advisorJabeen, Shabnam
dc.contributor.authorBapanapalli, Abhinav
dc.contributor.authorMahmud, Ateef
dc.contributor.authorNadig, Chiraag
dc.contributor.authorThakker, Krish
dc.date.accessioned2025-01-15T17:55:20Z
dc.date.available2025-01-15T17:55:20Z
dc.date.issued2024
dc.description.abstractPredicting stock price movements is a complex challenge faced by many traders and analysts. Our research leverages Quantum Gramian Angular Field (QGAF) transformations combined with Convolutional Neural Networks (CNNs) to classify stock price trends as "up" or "down." By transforming 1D time-series stock data into 2D images, we enable CNNs to extract features more effectively, showcasing the potential of quantum machine learning in financial forecasting.
dc.identifierhttps://doi.org/10.13016/oq13-a20l
dc.identifier.urihttp://hdl.handle.net/1903/33565
dc.language.isoen_US
dc.subjectThe First-Year Innovation & Research Experience (FIRE)
dc.subjectFIRE Quantum Machine Learning
dc.subjectQuantum Circuits
dc.subjectMachine Learning
dc.subjectFinancial Forecasting
dc.subjectTime-Series Analysis
dc.titleQuantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field And CNNs for Stock Return Predictions
dc.typeOther

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